Abstract Game-based learning (GBL) environments are designed to foster emotional experiences conducive to learning; yet, there are mixed findings regarding their effectiveness. The inconsistent results may stem from challenges in measuring and modeling emotions as multi-dimensional constructs during GBL. Traditional approaches often use one data channel and conventional statistics to study emotions, which limit our understanding of the multi-componential interactions that underlie emotional states during GBL. In this study, we merged non-linear dynamical systems (NLDS) theory with the component process model of emotion to examine interactions and synchrony among two emotion signals during GBL, facial expressions and heart rate variability (HRV), and assessed its relation to knowledge and learning gain. Data were collected from 58 participants (n = 58) at a university in Central Finland while they learned about pathology with a tower defense game called Antidote COVID-19. Results showed a significant improvement in knowledge after GBL. A NLDS technique called cross-wavelet transformation showed there were varying degrees of synchrony between facial expressions and HRV. Neutral expressions showed the highest degree of synchrony with HRV, followed closely by happiness and anger with HRV. However, the synchrony between facial expressions and HRV did not affect knowledge and learning gain. This research contributes to the field by studying emotions as multidimensional systems during GLB.

Gamification is commonly employed to support the formation of positive psychological states and learning outcomes, with one of the primary psychological factors chiefly relevant to learning being the flow state. However, the effects of gamification on students’ flow experience are still little known. Filling this gap, we conducted a between-subjects controlled experiment (N = 65) to analyze the effects of gamification on students’ flow experience. Using descriptive and inferential statistical techniques, we compared the flow experience between participants who used a gamified version of an educational system (experimental group) and a group that used the same system without gamification (control group). The main results indicate that the employed gamification design did not affect students’ flow experience. Our study contributes especially to educational technologies and gamification fields, demonstrating that gamification may not affect students’ flow experience.

In recent years, numerous studies have investigated the effects of gamification on different students’ experiences (e.g., motivation, engagement, and accomplishment). However, the effects of gamification on students’ academic performance are still a mystery. To address this gap, we conducted a between-subjects controlled experiment (N = 71) to analyze the effects of gamification on students’ academic performance. Using descriptive and inferential statistical techniques, we compared the academic performance of participants who used a gamified version of an educational system (experimental group) and a group that used the same system without gamification (control group). The main results indicate that the student’s academic performance was significantly higher in the experimental group (i.e., students who used the gamified version of the system). We contribute to the fields of educational technology and gamification by providing new evidence about the effects of gamification on the actual students’ academic performance.

This article explores the potential of emotional mimicry in identifying the leader and follower students in collaborative learning settings. Our data include video recorded interactions of 24 high school students who worked together in groups of three during a collaborative exam. A facial emotions recognition method was used to capture participants’ facial emotions during the collaborative work. Cross-recurrence quantification analysis was applied on the detected facial emotions to see the level and direction of emotional mimicry among the dyads in the same groups. In order to validate the cross-recurrence quantification analysis results, student interactions in terms of leading or following the task were video coded. Our findings showed that the leaders and followers identified by cross-recurrence quantification analysis findings matched the leaders and followers identified by the video coding in 70 percent of the dyadic interactions across the collaborating groups. The current findings show that video-based facial emotions recognition as a method can add to collaborative learning research, especially explaining some social, and affective dynamics about it. The study further discusses the possible variables that might confound the relationship between emotional mimicry and leader-follower interactions during collaboration.